Results 11 to 20 of about 1,008,663 (311)

Optimizing computed tomography image reconstruction for focal hepatic lesions: Deep learning image reconstruction vs iterative reconstruction [PDF]

open access: yesHeliyon
Background: Deep learning image reconstruction (DLIR) is a novel computed tomography (CT) reconstruction technique that minimizes image noise, enhances image quality, and enables radiation dose reduction.
Varin Jaruvongvanich   +10 more
doaj   +2 more sources

Deep learning-based video stream reconstruction in mass-production diffractive optical systems [PDF]

open access: yesКомпьютерная оптика, 2021
Many recent studies have focused on developing image reconstruction algorithms in optical systems based on flat optics. These studies demonstrate the feasibility of applying a combination of flat optics and the reconstruction algorithms in real vision ...
V. Evdokimova   +12 more
doaj   +1 more source

Value of Virtual Reality Technology in Image Inspection and 3D Geometric Modeling

open access: yesIEEE Access, 2020
Aiming at the poor expressive ability of image statistical information during the reconstruction process of traditional 3D image reconstruction method based on virtual reality technology, resulting in low accuracy of 3D image after reconstruction, a new ...
Longyu Lu, Jinkai Ma, Shuying Qu
doaj   +1 more source

Variational semi-blind sparse deconvolution with orthogonal kernel bases and its application to MRFM [PDF]

open access: yes, 2013
We present a variational Bayesian method of joint image reconstruction and point spread function (PSF) estimation when the PSF of the imaging device is only partially known.
Almeida   +39 more
core   +10 more sources

Sparse Image Reconstruction for Molecular Imaging [PDF]

open access: yesIEEE Transactions on Image Processing, 2009
12 pages, 8 ...
Raviv Raich   +2 more
openaire   +3 more sources

On Hallucinations in Tomographic Image Reconstruction

open access: yesIEEE Transactions on Medical Imaging, 2021
Tomographic image reconstruction is generally an ill-posed linear inverse problem. Such ill-posed inverse problems are typically regularized using prior knowledge of the sought-after object property. Recently, deep neural networks have been actively investigated for regularizing image reconstruction problems by learning a prior for the object ...
Sayantan Bhadra   +3 more
openaire   +5 more sources

Generalized Image Reconstruction in Optical Coherence Tomography Using Redundant and Non-Uniformly-Spaced Samples

open access: yesSensors, 2021
In this paper, we use Frame Theory to develop a generalized OCT image reconstruction method using redundant and non-uniformly spaced frequency domain samples that includes using non-redundant and uniformly spaced samples as special cases. We also correct
Karim Nagib   +4 more
doaj   +1 more source

Computational Imaging for VLBI Image Reconstruction [PDF]

open access: yes2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016
Accepted for publication at CVPR 2016, Project Website: http://vlbiimaging.csail.mit.edu/, Video of Oral Presentation at CVPR June 2016: https://www.youtube.com/watch?v ...
Bouman, Katherine L.   +5 more
openaire   +6 more sources

Metal Artifact Reduction in CT: Where Are We After Four Decades?

open access: yesIEEE Access, 2016
Methods to overcome metal artifacts in computed tomography (CT) images have been researched and developed for nearly 40 years. When X-rays pass through a metal object, depending on its size and density, different physical effects will negatively affect ...
Lars Gjesteby   +6 more
doaj   +1 more source

Image reconstruction by linear programming [PDF]

open access: yesIEEE Transactions on Image Processing, 2005
One way of image denoising is to project a noisy image to the subspace of admissible images derived, for instance, by PCA. However, a major drawback of this method is that all pixels are updated by the projection, even when only a few pixels are corrupted by noise or occlusion.
Tsuda, K., Rätsch, G.
openaire   +6 more sources

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